Performance Analysis for ROUGE And F-Measure in Arabic Text Summarization

Authors

  • Hawraa Ali Taher Department of Computer Science, Faculty of Education for Girls, University of Kufa, Najaf, Iraq https://orcid.org/0000-0003-3375-3677
  • Hasanain Ali Hussein University of Kufa-Information Technology Research and Development Center, Iraq
  • Sabah Khudhair Abbas College of Imam Kadhim for Islamic Sciences University, Iraq

Keywords:

Arabic Language, text summarization, ROUGE, F-measure, preprocessing

Abstract

Summarizing a document has become a necessity as, because so much information is produced every day. Document summary makes it simpler to understand the text document than it would be to read through a collection of documents. A foundation for creating an  condensed version of one or more text documents is provided by text summary. It is a crucial method for finding pertinent information on the Internet or in sizable text libraries. Additionally, it is essential to extract data in a way that the user would find the information interesting. Extractive summarization and abstract summarization are the two basic approaches used for text summarizing. In order to create the summary, the extractive summarization method chooses the sentences from a Word document and arranges them according to their weight. Abstractive summarizing is a technique that takes the key ideas from a document's content and expresses them abstractly in plain English. Numerous summary methods have been created on the foundation of these two approaches. There are numerous techniques that are language-specific exclusively. In this paper, we used extractive summarization methods and got good results.

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References

S. Pawar, & S. Rathod, (2020). Text summarization using cosine similarity and clustering approach (No. 2927). EasyChair.

A. Haboush , M. Al-Zoubi, A. Momani, & M. Tarazi, (2012). Arabic text summarization model using clustering techniques. World of Computer Science and Information Technology Journal (WCSIT) ISSN, 2221-0741.

N. J. Kadhim, & Saleh, H. H. (2018). Improving Extractive Multi-Document Text Summarization Through Multi-Objective Optimization. Iraqi Journal of Science, 2135-2149.

A. Qaroush , I. A. Farha, W. Ghanem, M. Washaha &E. Maali (2019). An efficient single document Arabic text summarization using a combination of statistical and semantic features. Journal of King Saud University-Computer and Information Sciences.

L. Al Qassem, D. Wang, H. Barada, A. Al-Rubaie, & Almoosa, N. (2019, September). Automatic Arabic text summarization based on fuzzy logic. In Proceedings of the 3rd international conference on natural language and speech processing (pp. 42-48).

Q. A. Al-Radaideh, & D. Q. Bataineh (2018). A hybrid approach for arabic text summarization using domain knowledge and genetic algorithms. Cognitive Computation, 10(4), 651-669.

R. Belkebir & A. Guessoum, (2015). A supervised approach to arabic text summarization using adaboost. In New contributions in information systems and technologies (pp. 227-236). Springer, Cham.

M. Attia, (2007, June). Arabic tokenization system. In Proceedings of the 2007 workshop on computational approaches to semitic languages: Common issues and resources (pp. 65-72).

L. S. Larkey, L. Ballesteros & M. E. Connell (2007). Light stemming for Arabic information retrieval. In Arabic computational morphology (pp. 221-243). Springer, Dordrecht.

F. Rahutomo, T. Kitasuka, & M. Aritsugi, (2012). Semantic Cosine Similarity. In The 7th International Student Conference on Advanced Science and Technology ICAST.

D. Wali,, &N. Modhe, (2015). Word Sense Disambiguation Algorithms in Hindi.

V. Siivola,, & M. L. Pellom,. (2005). Growing an n-gram language model. In Ninth European conference on speech communication and technology.‏

F. T .AL-Khawaldeh, & V. W. Samawi, (2015). Lexical cohesion and entailment based segmentation for arabic text summarization (lceas). World of Computer Science & Information Technology Journal, 5(3).

W. Kryscinski, R. Paulus, C. Xiong, &R. Socher, (2018). Improving abstraction in text summarization. arXiv preprint arXiv:1808.07913.

Generalized structure of text summarization

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Published

17.02.2023

How to Cite

[1]
H. . Ali Taher, H. . Ali Hussein, and S. . Khudhair Abbas, “Performance Analysis for ROUGE And F-Measure in Arabic Text Summarization ”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 40–45, Feb. 2023.

Issue

Vol. 11 No. 2 (2023): (Article in Press)

Section

Research Article

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